{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.cluster import KMeans\n",
    "from scipy.spatial.distance import cdist\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MEMBER_NO</th>\n",
       "      <th>FFP_DATE</th>\n",
       "      <th>FIRST_FLIGHT_DATE</th>\n",
       "      <th>GENDER</th>\n",
       "      <th>FFP_TIER</th>\n",
       "      <th>WORK_CITY</th>\n",
       "      <th>WORK_PROVINCE</th>\n",
       "      <th>WORK_COUNTRY</th>\n",
       "      <th>AGE</th>\n",
       "      <th>LOAD_TIME</th>\n",
       "      <th>...</th>\n",
       "      <th>ADD_Point_SUM</th>\n",
       "      <th>Eli_Add_Point_Sum</th>\n",
       "      <th>L1Y_ELi_Add_Points</th>\n",
       "      <th>Points_Sum</th>\n",
       "      <th>L1Y_Points_Sum</th>\n",
       "      <th>Ration_L1Y_Flight_Count</th>\n",
       "      <th>Ration_P1Y_Flight_Count</th>\n",
       "      <th>Ration_P1Y_BPS</th>\n",
       "      <th>Ration_L1Y_BPS</th>\n",
       "      <th>Point_NotFlight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>54993</td>\n",
       "      <td>2006-11-02</td>\n",
       "      <td>2008-12-24</td>\n",
       "      <td>男</td>\n",
       "      <td>6</td>\n",
       "      <td>.</td>\n",
       "      <td>北京</td>\n",
       "      <td>CN</td>\n",
       "      <td>31.0</td>\n",
       "      <td>2014-03-31</td>\n",
       "      <td>...</td>\n",
       "      <td>39992</td>\n",
       "      <td>114452</td>\n",
       "      <td>111100</td>\n",
       "      <td>619760</td>\n",
       "      <td>370211</td>\n",
       "      <td>0.509524</td>\n",
       "      <td>0.490476</td>\n",
       "      <td>0.487221</td>\n",
       "      <td>0.512777</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>28065</td>\n",
       "      <td>2007-02-19</td>\n",
       "      <td>2007-08-03</td>\n",
       "      <td>男</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>北京</td>\n",
       "      <td>CN</td>\n",
       "      <td>42.0</td>\n",
       "      <td>2014-03-31</td>\n",
       "      <td>...</td>\n",
       "      <td>12000</td>\n",
       "      <td>53288</td>\n",
       "      <td>53288</td>\n",
       "      <td>415768</td>\n",
       "      <td>238410</td>\n",
       "      <td>0.514286</td>\n",
       "      <td>0.485714</td>\n",
       "      <td>0.489289</td>\n",
       "      <td>0.510708</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>55106</td>\n",
       "      <td>2007-02-01</td>\n",
       "      <td>2007-08-30</td>\n",
       "      <td>男</td>\n",
       "      <td>6</td>\n",
       "      <td>.</td>\n",
       "      <td>北京</td>\n",
       "      <td>CN</td>\n",
       "      <td>40.0</td>\n",
       "      <td>2014-03-31</td>\n",
       "      <td>...</td>\n",
       "      <td>15491</td>\n",
       "      <td>55202</td>\n",
       "      <td>51711</td>\n",
       "      <td>406361</td>\n",
       "      <td>233798</td>\n",
       "      <td>0.518519</td>\n",
       "      <td>0.481481</td>\n",
       "      <td>0.481467</td>\n",
       "      <td>0.518530</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>21189</td>\n",
       "      <td>2008-08-22</td>\n",
       "      <td>2008-08-23</td>\n",
       "      <td>男</td>\n",
       "      <td>5</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>CA</td>\n",
       "      <td>US</td>\n",
       "      <td>64.0</td>\n",
       "      <td>2014-03-31</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>34890</td>\n",
       "      <td>34890</td>\n",
       "      <td>372204</td>\n",
       "      <td>186100</td>\n",
       "      <td>0.434783</td>\n",
       "      <td>0.565217</td>\n",
       "      <td>0.551722</td>\n",
       "      <td>0.448275</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>39546</td>\n",
       "      <td>2009-04-10</td>\n",
       "      <td>2009-04-15</td>\n",
       "      <td>男</td>\n",
       "      <td>6</td>\n",
       "      <td>贵阳</td>\n",
       "      <td>贵州</td>\n",
       "      <td>CN</td>\n",
       "      <td>48.0</td>\n",
       "      <td>2014-03-31</td>\n",
       "      <td>...</td>\n",
       "      <td>22704</td>\n",
       "      <td>64969</td>\n",
       "      <td>64969</td>\n",
       "      <td>338813</td>\n",
       "      <td>210365</td>\n",
       "      <td>0.532895</td>\n",
       "      <td>0.467105</td>\n",
       "      <td>0.469054</td>\n",
       "      <td>0.530943</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 44 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   MEMBER_NO   FFP_DATE FIRST_FLIGHT_DATE GENDER  FFP_TIER    WORK_CITY  \\\n",
       "0      54993 2006-11-02        2008-12-24      男         6            .   \n",
       "1      28065 2007-02-19        2007-08-03      男         6          NaN   \n",
       "2      55106 2007-02-01        2007-08-30      男         6            .   \n",
       "3      21189 2008-08-22        2008-08-23      男         5  Los Angeles   \n",
       "4      39546 2009-04-10        2009-04-15      男         6           贵阳   \n",
       "\n",
       "  WORK_PROVINCE WORK_COUNTRY   AGE  LOAD_TIME       ...         ADD_Point_SUM  \\\n",
       "0            北京           CN  31.0 2014-03-31       ...                 39992   \n",
       "1            北京           CN  42.0 2014-03-31       ...                 12000   \n",
       "2            北京           CN  40.0 2014-03-31       ...                 15491   \n",
       "3            CA           US  64.0 2014-03-31       ...                     0   \n",
       "4            贵州           CN  48.0 2014-03-31       ...                 22704   \n",
       "\n",
       "   Eli_Add_Point_Sum  L1Y_ELi_Add_Points  Points_Sum  L1Y_Points_Sum  \\\n",
       "0             114452              111100      619760          370211   \n",
       "1              53288               53288      415768          238410   \n",
       "2              55202               51711      406361          233798   \n",
       "3              34890               34890      372204          186100   \n",
       "4              64969               64969      338813          210365   \n",
       "\n",
       "   Ration_L1Y_Flight_Count  Ration_P1Y_Flight_Count  Ration_P1Y_BPS  \\\n",
       "0                 0.509524                 0.490476        0.487221   \n",
       "1                 0.514286                 0.485714        0.489289   \n",
       "2                 0.518519                 0.481481        0.481467   \n",
       "3                 0.434783                 0.565217        0.551722   \n",
       "4                 0.532895                 0.467105        0.469054   \n",
       "\n",
       "  Ration_L1Y_BPS  Point_NotFlight  \n",
       "0       0.512777               50  \n",
       "1       0.510708               33  \n",
       "2       0.518530               26  \n",
       "3       0.448275               12  \n",
       "4       0.530943               39  \n",
       "\n",
       "[5 rows x 44 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#导入原始数据\n",
    "data=pd.read_csv('D:\\\\xy\\\\datas\\\\chapter7\\\\demo\\\\data\\\\air_data.csv',parse_dates=[1,2,9])\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#数据探索\n",
    "explore=data.describe().T\n",
    "explore['null']=len(data)-explore['count']\n",
    "explore=pd.DataFrame(explore[['null','max','min']])\n",
    "explore.to_excel('D:\\\\xy\\\\datas\\\\chapter7\\\\demo\\\\data\\\\explore.xls')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel\\__main__.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    }
   ],
   "source": [
    "#选取模型指标并更新入df表\n",
    "L=(data['LOAD_TIME']-data['FFP_DATE']).dt.days\n",
    "df=data[['LAST_TO_END','FLIGHT_COUNT','SEG_KM_SUM','avg_discount']]\n",
    "df['L']=L\n",
    "df.columns=['R','F','M','C','L']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>R</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "      <th>C</th>\n",
       "      <th>L</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.952660</td>\n",
       "      <td>14.104488</td>\n",
       "      <td>26.887901</td>\n",
       "      <td>1.294751</td>\n",
       "      <td>1.441178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.920020</td>\n",
       "      <td>9.122093</td>\n",
       "      <td>13.193844</td>\n",
       "      <td>2.862354</td>\n",
       "      <td>1.312523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.898260</td>\n",
       "      <td>8.766208</td>\n",
       "      <td>12.718386</td>\n",
       "      <td>2.875087</td>\n",
       "      <td>1.333768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.430416</td>\n",
       "      <td>0.794378</td>\n",
       "      <td>12.605032</td>\n",
       "      <td>1.991687</td>\n",
       "      <td>0.663343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.930900</td>\n",
       "      <td>9.976218</td>\n",
       "      <td>13.969099</td>\n",
       "      <td>1.343389</td>\n",
       "      <td>0.390687</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          R          F          M         C         L\n",
       "0 -0.952660  14.104488  26.887901  1.294751  1.441178\n",
       "1 -0.920020   9.122093  13.193844  2.862354  1.312523\n",
       "2 -0.898260   8.766208  12.718386  2.875087  1.333768\n",
       "3 -0.430416   0.794378  12.605032  1.991687  0.663343\n",
       "4 -0.930900   9.976218  13.969099  1.343389  0.390687"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#将数据标准化处理\n",
    "zs_df=(df-df.mean())/df.std()\n",
    "zs_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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NQSEDzUOBfwEPl8JgswearVCTJqWkMXQovPBCOta7N+yzD+y5JyyxRL7xmTWl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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ac8225bba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#使用肘部观察法确定簇的数量K\n",
    "k=np.arange(1,10)\n",
    "meandist=[]\n",
    "for i in k:\n",
    "    kmeans=KMeans(n_clusters=i,n_jobs=8,max_iter=300)\n",
    "    kmeans.fit(zs_df)\n",
    "    meandist.append(sum(np.min(cdist(zs_df,kmeans.cluster_centers_,'euclidean'),axis=1))/zs_df.shape[0])\n",
    "plt.plot(k,meandist,'bx-')\n",
    "plt.xlabel('k')\n",
    "plt.ylabel('Average Dispersion')\n",
    "plt.title('Selecting k with Elbow Method')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
       "    n_clusters=5, n_init=10, n_jobs=8, precompute_distances='auto',\n",
       "    random_state=None, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#使用K-means算法训练模型\n",
    "from sklearn.cluster import KMeans\n",
    "k=5\n",
    "kmeans=KMeans(n_clusters=k,n_jobs=8,max_iter=300)\n",
    "kmeans.fit(zs_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>R</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "      <th>C</th>\n",
       "      <th>L</th>\n",
       "      <th>数目</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>分群1</th>\n",
       "      <td>-0.422759</td>\n",
       "      <td>-0.156704</td>\n",
       "      <td>-0.157224</td>\n",
       "      <td>-0.247266</td>\n",
       "      <td>-0.696542</td>\n",
       "      <td>24958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>分群2</th>\n",
       "      <td>-0.380093</td>\n",
       "      <td>-0.083879</td>\n",
       "      <td>-0.091844</td>\n",
       "      <td>-0.153239</td>\n",
       "      <td>1.165651</td>\n",
       "      <td>15894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>分群3</th>\n",
       "      <td>-0.807893</td>\n",
       "      <td>2.497316</td>\n",
       "      <td>2.439791</td>\n",
       "      <td>0.309171</td>\n",
       "      <td>0.487712</td>\n",
       "      <td>5362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>分群4</th>\n",
       "      <td>1.663451</td>\n",
       "      <td>-0.572758</td>\n",
       "      <td>-0.536597</td>\n",
       "      <td>-0.178673</td>\n",
       "      <td>-0.323974</td>\n",
       "      <td>12578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>分群5</th>\n",
       "      <td>0.001149</td>\n",
       "      <td>-0.224092</td>\n",
       "      <td>-0.225749</td>\n",
       "      <td>2.205735</td>\n",
       "      <td>0.078285</td>\n",
       "      <td>4196</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            R         F         M         C         L     数目\n",
       "分群1 -0.422759 -0.156704 -0.157224 -0.247266 -0.696542  24958\n",
       "分群2 -0.380093 -0.083879 -0.091844 -0.153239  1.165651  15894\n",
       "分群3 -0.807893  2.497316  2.439791  0.309171  0.487712   5362\n",
       "分群4  1.663451 -0.572758 -0.536597 -0.178673 -0.323974  12578\n",
       "分群5  0.001149 -0.224092 -0.225749  2.205735  0.078285   4196"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#简单输出聚类结果\n",
    "r1=pd.Series(kmeans.labels_).value_counts()\n",
    "r2=pd.DataFrame(kmeans.cluster_centers_)\n",
    "r=pd.concat([r2,r1],axis=1)\n",
    "r.columns=list(df.columns)+['数目']\n",
    "r.index=['分群1','分群2','分群3','分群4','分群5']\n",
    "r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#输出原始数据及其类别\n",
    "h=pd.concat([df,pd.Series(kmeans.labels_,index=df.index,name=u'聚类类别')],axis=1)\n",
    "h.to_excel('D:\\\\xy\\datas\\\\chapter7\\\\demo\\\\data\\\\outputfile.xls')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#自定义类别各属性概率密度图函数\n",
    "def density_plot(data,title):\n",
    "    import matplotlib.pyplot as plt\n",
    "    plt.title(u'聚类类别%s各属性的密度曲线'%title)\n",
    "    plt.rcParams['font.sans-serif']=['SimHei']\n",
    "    plt.rcParams['axes.unicode_minus']=False\n",
    "    for i in range(len(data.iloc[0])):\n",
    "        (data.iloc[:,i]).plot(kind='density',label=data.columns[i],linewidth=2)\n",
    "        plt.legend()\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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KGWMHpTI0Y3qVv/zlL633b7311qSf3670Nt3S2NzC5t31iMCw/s6chUgL461Nu1MZmjEm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\nYzyxhGGMMcaT/x+u0a8X0z3RcAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ac8ba392e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ac8becd9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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/R69m6azUVvu3fmUzf/fvz+leLjJhKLFI4Dq6ezjd2kUyYRqxDGNlXTU/+uh1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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ac8bfcb7b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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+KP/ln3bR0x/ld26ay5qF0/0OKSPNLCtkRmkB7V19HGvu9DscSYESgWSNb770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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ac8b680dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ac8b682d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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SiPYB7/aEYWZUaOBbJCOlImHUAIXh85IuXiNenbjHmVkWcBLwVApilT7YUd8EwLCi3ZdQ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\nRAlDREQi+f+oYqwDAdcdNAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ac8b69a4a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ac8bb36128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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h//z+GQB88d7nae3sCbkikcwpeMRTzjkOnUwEz9TxpSFXkx8+8tY5vGXGeA41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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ac8b688fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ac8bdf2358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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2E6mgzARuFZFXReQp4GZgWeTvVhC+y+PcKG2DoQu4HQhG/jtaDqdt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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ac8bd6a358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AEJnyFDAklqETD5aXYcxrzlBj4XxSuXNRicjUpIAhsQyVpMr7yNSmagav5711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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ac8c06a710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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CKu38j++8xq/cv4c3OvujrppI0SgwZMZOJZI89P1M6+Iz716YrYt8qivi7Hj/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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ac83037198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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lT9wNiIj6lS7qV7qkol9asxERkchpZCMiIpFT2IiISOQUNhEws5Vm9gMze9TM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\nIpFT2IiISOQUNiIiEjmFjYiIRO7/A/ePSc2BCO4iAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ac8bdaf550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Ck2I3tvNoReCP1TyjArN4L2QkGqM0ovKZFI905+R9Dvgn4IueWHppZl8JLCoRxorFQS4m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\nIvf/AUumiuY+w7bSAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ac8b9b0fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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7OwZxzRV0P2FyI7AMOOSgztUOy35N+Bj/3/W43lJgtYhcDxwDPqyqHYOI3bjI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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ac8b59fcf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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JxA8udXf2fX7pdtsGxURkScT4yvbyo4DNzPKLswZ1pygvk837j7B0a7nX4Rgf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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ac8bd81cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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g4nLHVE4xdRRgfkdmB9LjowkmktqBVKTZKAgibrpDaQIt8RjL53fgDoPHx6pVmohUiYIg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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ac830373c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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3TL7zL7/la4+/xG8GjvLXD/+aLz+ym2sufhNb3nkOb+5ZBsDOvYN87h+eY9cr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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ac8bca69e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ac8be15eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ac8bf46ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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UVReqathLyYWq42W7SOqZ2Gors7PqZaQGmD2yOwCL3rcuLWO8aKof4kn33x/E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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ac8282c6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AZ82fTkC/ofomkGWsW1zFusVVHGrvZtPf9/LLJ9+krrmLm//4Ev/5591curyG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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ac8b9e00b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ac8b5af630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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/JNxZxbFSkpPOmvmlrJlfOvCxtq5e9tW0sbemjb2nWtlb08aRhnaON3VyvKmT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\nLI0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ac8b6d8a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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/iEt/u5D5a7SnpL1SEpGYNA5nZSmJyPGG9spi7r+dxiXjC6iqqWf2H5cyb/Vu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\nWQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ac8b68b940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#输出各类别各属性概率密度图\n",
    "for i in range(k):\n",
    "    density_plot(df[h[u'聚类类别']==i],i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>R</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "      <th>C</th>\n",
       "      <th>L</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>聚类类别</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>106.152721</td>\n",
       "      <td>10.666309</td>\n",
       "      <td>15213.421716</td>\n",
       "      <td>0.692750</td>\n",
       "      <td>2473.584841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>98.219754</td>\n",
       "      <td>9.654311</td>\n",
       "      <td>13860.301175</td>\n",
       "      <td>0.674374</td>\n",
       "      <td>894.654351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>177.970374</td>\n",
       "      <td>8.513674</td>\n",
       "      <td>12066.464221</td>\n",
       "      <td>1.120212</td>\n",
       "      <td>1531.355287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>482.237426</td>\n",
       "      <td>3.793230</td>\n",
       "      <td>5881.737426</td>\n",
       "      <td>0.687104</td>\n",
       "      <td>1210.796423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>27.597132</td>\n",
       "      <td>46.906500</td>\n",
       "      <td>68231.306575</td>\n",
       "      <td>0.779568</td>\n",
       "      <td>1898.685975</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               R          F             M         C            L\n",
       "聚类类别                                                            \n",
       "0     106.152721  10.666309  15213.421716  0.692750  2473.584841\n",
       "1      98.219754   9.654311  13860.301175  0.674374   894.654351\n",
       "2     177.970374   8.513674  12066.464221  1.120212  1531.355287\n",
       "3     482.237426   3.793230   5881.737426  0.687104  1210.796423\n",
       "4      27.597132  46.906500  68231.306575  0.779568  1898.685975"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看各类别各属性的均值\n",
    "h_group=h.groupby(['聚类类别']).mean()\n",
    "h_group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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00JeZG6hm9keo7lvYAXyZ6uPObsjMr2fmtzs5rn7qm4DTgKPA+5lF1zmrbM7YVYIzqK49\nP4Xq7QHeTnXt9X9SBe8WYDAiPlHXrqa6Me1dmfmHEXEu8NtUyy10clxEnA38LHA21S8VGPloSKlR\nBrtmvcx8JSJ+E/gocC/wJNV7At1d13qAx4FzqD5k/RKq8D89Ii6sh1kUEZcBL3d43PNUd2teBOwF\nFlDdyBL1l9QYb1DSrBcRb6e6CuYbwBtU4Xwd1Zr71Zm5Z8zxpwBfBA4Bf5KZ/zDOuBMeV6+pP0f1\nbqE/CbyHan39r4H76yUdadq5xq4SXAB8gWp2vYjqzc3+nOpE58cj4g8i4syI6ImIXwAepVo7vwbY\nGhGfiIgfPz5YJ8fVob+D6uTsfOCdwHBmfpjqcssl0/EPl1ox2DXrZeYjwC8CuzPzA1QnMRdm5t8A\nm4GlwHepgvpHgV/KzKcy838y89eAvwfuioizImJ5J8fVz7c/Mz8KXAisowp5qD7Y/Ynp+ddLJ3Ip\nRpIK44xdkgpjsEtSYQx2SSqMwS5JhTHYJakwBrskFeb/ATFyKdZSLhnxAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1cdd33f97f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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CjLgkFWbEJakwIy5JhRlxSSrsfwFTVSsJePbKZQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1cdd3365eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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GwEP7YpK3MfjMouMM7lx+JnAd8Lo+G7bIbgBuBV7UXb7bsvuT/EhVfbSqPpnkOcC7GYwM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\nUQaAJDXqfwH8ppQrP8cA8AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1cdd283d630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1cdd33b78d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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NnqqohcWRvU5I/fTJcroR/BbgzCSvpjut8nLgemBFVU3S/QWwn+66ii3AF+i+au5NVfWN\nqvruKO36t34zcBrwCPA6jqPzsHVic2SvE9UZdOfGP5Xu1gfPoDs3/Jt0YbwB2J3k/X3tMrqL6V5Q\nVb+f5HnAb9JN1TBKuyTPAX4OeA7dBw1Mfy2nNCjDXiekqnowya8D7wY+CHyO7h5I7+xrS4BbgecC\nzwIupPtAeHqSl/SrWZrkYuC+Edt9he6q05cCtwMn0V18k/4hDcaLqnRCSvIMurNvHgAepQvsK+jm\n8C+rqh2HtH8q8GlgL/CnVfWPR1jvE7br5+jvpLuL6s8Ar6Sbr78RuKGfDpLmnXP2OlG9CPgU3Sh8\nKd0N3v6M7mDq+5L8XpJnJlmS5BeAT9LNxV8ObEzy/iQ/fWBlo7TrPwi20B0AXgz8FDBVVe+gO/Vz\n2XxsuDQTw14npKq6BfhFYHtV/Q7dgdKTq+qjwHrgdOAHdOH9E8AvVdVtVfW/VfWrwN8Db09ybpIz\nR2nXv9+9VfVu4CXABXTBD3Az8Nn52XrpcE7jSFIDHNlLUgMMe0lqgGEvSQ0w7CWpAYa9JDXAsJek\nBvw/qkeCDameHSMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1cdd2f95940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#绘制不同类别在不同属性上的均值的直方图\n",
    "for k in h_group.columns:\n",
    "    import matplotlib.pyplot as plt\n",
    "    plt.rcParams['font.sans-serif']=['SimHei']\n",
    "    plt.rcParams['axes.unicode_minus']=False\n",
    "    h_group[k].plot(kind='bar',alpha=0.3,color='b')\n",
    "    plt.legend(loc='best')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
